Growth faltering and recovery in children aged 1–8 years in four low- and middle-income countries: Young Lives
Bibliographic record
Abstract
OBJECTIVE: We characterized post-infancy child growth patterns and determined the incidence of becoming stunted and of recovery from stunting. DESIGN: Data came from Young Lives, a longitudinal study of childhood poverty in four low- and middle-income countries. SETTING: We analysed length/height measurements for children at ages 1, 5 and 8 years. SUBJECTS: Children (n 7171) in Ethiopia, India, Peru and Vietnam. RESULTS: Mean height-for-age Z-score (HAZ) at age 1 year ranged from -1·51 (Ethiopia) to -1·08 (Vietnam). From age 1 to 5 years, mean HAZ increased by 0·27 in Ethiopia (P < 0·001) and decreased among the other cohorts (range: -0·19 (Peru) to -0·32 (India); all P < 0·001). From 5 to 8 years, mean HAZ increased in all cohorts (range: 0·19 (India) to 0·38 (Peru); all P < 0·001). Prevalence of stunting (HAZ<-2·0) at 1 year ranged from 21 % (Vietnam) to 46 % (Ethiopia). From age 1 to 5 years, stunting prevalence decreased by 15·1 percentage points in Ethiopia (P < 0·001) and increased in the other cohorts (range: 3·0 percentage points (Vietnam) to 5·3 percentage points (India); all P ≤ 0·001). From 5 to 8 years, stunting prevalence decreased in all cohorts (range: 5·0 percentage points (Vietnam) to 12·7 percentage points (Peru); all P < 0·001). The incidence of becoming stunted between ages 1 to 5 years ranged from 11 % (Vietnam) to 22 % (India); between ages 5 to 8 years, it ranged from 3 % (Peru) to 6 % (India and Ethiopia). The incidence of recovery from stunting between ages 1 and 5 years ranged from 27 % (Vietnam) to 53 % (Ethiopia); between ages 5 and 8 years, it ranged from 30 % (India) to 47 % (Ethiopia). CONCLUSIONS: We found substantial recovery from early stunting among children in four low- and middle-income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".